In this seminar, you will learn how to use ArcGIS Workflow Manager to organize, centralize, and manage your GIS operations and integrate them with your non-GIS workflows in a flexible and distributed environment.This seminar was developed to support the following:ArcGIS Desktop 10.2 (Standard Or Advanced)ArcGIS Workflow Manager for Desktop

In this seminar, you will learn how ArcGIS Workflow Manager helps you organize, centralize, and standardize workflows in a flexible and distributed environment.This seminar was developed to support the following:ArcGIS Workflow Manager for Desktop

ArcGIS HUB is a great site for managing multiple resources for a community. However, the workflows for managing content focus on content that is already in your ArcGIS Online organisation. This model doesnt work as well when you want to add content from multiple organisations, or when the object you want to add are outside of the wider ArcGIS ecosystem. In such cases you may find you need to edit the html of cards to point to external resources. It is easy to make mistakes when editing code and some may not feel confident doing so.Here we present a workflow that can be used to add and manage content in your ArcGIS HUB without having to edit any code. The workflow involves:

In this seminar, the presenters will introduce essential concepts of Collector for ArcGIS and show how this app integrates with other components of the ArcGIS platform to provide a seamless data management workflow. You will also learn how anyone in your organization can easily capture and update data in the field, right from their smartphone or tablet.This seminar was developed to support the following:ArcGIS Desktop 10.2.2 (Basic)ArcGIS OnlineCollector for ArcGIS (Android) 10.4Collector for ArcGIS (iOS) 10.4Collector for ArcGIS (Windows) 10.4

Solutions Playbook for Mapping, Statistics, and Land Administration (MSL) consists of ArcGIS solutions for common workflows of national government accounts in the MSL sector.The contents of the playbook will be gradually completed and regularly updated by Esri Indonesia's Solution Strategist Team for National Government Sector.General Solutions:- Offline Mapping- Field Data Collection: Connecting ArcGIS Field Apps with External GNSS Receivers- Open DataNational Mapping and Charting:- Drone2Map for ArcGIS- Production Mapping- Product On DemandOfficial Statistics:- Workflow Manager- Data ReviewerAssessment, Tax, and Land Records:- Workflow Manager- Data Reviewer- Parcel FabricLast updated: Monday, 27 January 2020Copyright © 2020 Esri Indonesia. All rights reserved.

Descarga aquí el metadato:https://aplicaciones.siatac.co/geonetwork/srv/spa/catalog.search#/metadata/1742d666-50c8-4573-823e-5c5189ac0bbdDescarga aquí el shapefile:https://opendata.arcgis.com/datasets/31b4f21bfb6047659d5bc2b335d99eff_0.zipCorresponde a la capa de cicatrices por quemas en la Amazonía colombiana desde marzo del 2017 a escala 1:100.000. Para generar esta capa se seleccionan las imágenes satelitales, del programa LandSat; deben tener menos del 30% de nubes. Se hace una verificación de la cantidad puntos de calor detectados durante el mes de monitoreo, para corroborar cuales Path Row que cubren la región amazónica (4-57, 4-58, 4-59, 4-60, 4-61, 4-62, 4-63, 9-59, 9-60, 7-58, 7-59, 7-60, 7-61, 5-57, 5-58, 5-59, 5-60, 5-61, 5-62, 3-57, 3-58, 3-59, 8-58, 8-59, 8-60, 6-57, 6-58, 6-59, 6-60, 6-61, 6-62) deben priorizarse para la descarga.Para el procesamiento y clasificación de las imágenes, y los diferentes geoprocesos se usan herramientas del software ArcGis (Esri, 2022a). Con este programa se aplican los “Model Builder” que se han generado para este procesamiento, los cuales hacen parte de los flujos de trabajo (Workflow) construidos en la plataforma SIATAC. Con las imágenes se generan dos composiciones de color RGB , (1) una que integra el Índice de Vegetación de Diferencia Normalizada - NDVI (B5-B4/B5+B4), el Radio Normalizado de Quema-NBR (B5-B7/ (B5+B7) y la banda del infrarrojo cercano -IR (B5); (2) la otra composición se hace con las bandas B7-B5-B2; estas composiciones resaltan las áreas que han sufrido procesos de quema de la vegetación (Murcia & Otavo, 2018).Con la composición RGB (1) se hace una clasificación no supervisada tipo clúster (Clúster Iso) (Esri, 2022b) y se generan 11 clases. Sobre esta capa ráster se hace una verificación visual para determinar cuál de las 11 clases corresponde a las cicatrices, este proceso se hace con respaldo en el protocolo metodológico (Murcia et al., 2018) y las dos composiciones ya generadas. Una vez seleccionada la clase que se ha verificado como cicatrices, se hace una reclasificación binaria de las unidades, en la que uno (1) son cicatrices y cero (0) las otras clases. En el mismo proceso (Model Builder) se hace la vectorización y se genera la capa de polígonos de cicatrices.Luego se hace una verificación visual de los polígonos generados, para descartar aquellos que no son cicatrices, para esto se aplican los criterios previstos en el protocolo metodológico (Murcia et al., 2018) teniendo como referente las dos composiciones previamente generadas. Con la capa resultado se hace un proceso de análisis espacial de intersección (Esri, 2022c) para descartar las cicatrices que ya fueron clasificadas en el mes anterior.A la capa resultante se le hace control de calidad para verificar la exactitud temática, validando aspectos como delimitación, errores por omisión y errores por comisión. De igual modo, se verifica que la capa cumpla con todos los criterios de topología como la correcta adyacencia entre polígonos, y se aprueba la capa.En el siguiente paso, la capa aprobada se integra en un WorkFlow (Esri, 2022d) de la base de datos en la plataforma SIG de Esri, del SIATAC. Luego se aplica un proceso SIG de intersección mediante el cual se clasifican las cicatrices que se ubican en áreas que eran bosques, según la capa de bosques más reciente generada por el IDEAM (Ideam, 2022). Sobre los polígonos restantes, se aplica el mismo proceso SIG (intersección) con la capa de coberturas de la tierra, del periodo más reciente (Sinchi, 2022) y se clasifican las cicatrices que se ubican en donde había vegetación secundaria u otras coberturas, principalmente pastos.La capa resultante se somete a un proceso de análisis espacial de intersección para generar la información de las cicatrices con el tipo de cobertura vegetal afectada, por cada Unidad Espacial de Referencia (UER): Grandes paisajes, Jurisdicción de Corporaciones Autónomas Regionales o de Desarrollo sostenible, Estado legal del territorio, Departamentos y Municipios. Para finalizar, las estadísticas se publican en el portal del Sistema de Información Ambiental Territorial de la Amazonia colombiana -SIATAC (https://siatac.co/cicatrices-de-quema/).BIBLIOGRAFÍAMurcia, U. & Otavo, S. (2018). La amazonia se quema: Detección de áreas con mayor ocurrencia de incendios de vegetación como estrategia para la prevención y control. Revista Colombiana Amazónica No 11 de 2018, 59-72. https://sinchi.org.co/11-revista-colombia-amazonica.Cañon I., Gordillo G., León A., Murcia U., Romero H., Velásquez M. (2018). Protocolo para el monitoreo de cicatrices por quemas en la Amazonia colombiana. 46pp.Esri. (2022a). ArcGIS Desktop.https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview.Esri. (2022b). Clasificación no supervisada de clúster ISO.https://pro.arcgis.com/es/pro-app/2.8/tool-reference/spatial-analyst/iso-cluster-unsupervised-classification.htmEsri. (2022c). Intersección (Análisis).https://pro.arcgis.com/es/pro-app/latest/tool-reference/analysis/intersect.htmEsri. (2022d). ArcGIS Workflow Manager (Análisis).https://www.esri.com/en-us/arcgis/products/arcgis-workflow-manager/overviewIdeam. (2022). Sistema de Monitoreo de bosques y carbono SMBYC.https://smbyc.ideam.gov.co/MonitoreoBC-WEB/reg/indexLogOn.jspSinchi. (2022). Sistema de Monitoreo de las Coberturas de la tierra de la Amazonia colombiana SIMCOBA. Datos abiertos.https://datos.siatac.co/pages/coberturasDiccionario de datos:objectid: Corresponde al identificador propio de cada registro dentro de la capa de informaciónarea_ha: Corresponde al área en hectáreas de la unidad seleccionadaarea_km2: Corresponde al área en kilómetros cuadrados de la unidad seleccionadaano: Corresponde al año de publicación de la cicatriz de quemaorigen: Corresponde a la cobertura que fue afectada por la cicatriz de quemames: Corresponde al mes de publicación de la cicatriz de quemafecha_registro: Corresponde a la fecha de publicación de la cicatriz de quemashape: Corresponde a geometría del elementost_area(shape): Corresponde al área del elementost_length(shape): Corresponde al perímetro del elementoFuente:Modelos de Funcionamiento y Sostenibilidad del Laboratorio SIG y SRBogotá D.C., Colombia siatac.coCalle 20 # 5 - 44Código Postal: 110311 Teléfono: +57 (1) 4442060Horario de atención: 8:00 am - 5:00 pm de Lunes a Viernes Información de contacto:Establecer previo contacto telefónico o a través de correo electrónico, para realizar la solicitud o fijar una cita en el horario de atención.

A treatment is a specific location and scope of work or activity within a project. For example if a project was applying a microsurface to half of the project and chip seal to the other half, they would create two treatments, one identifying each activity or work type. Treatments do not correlate directly to routes but are often one-to-one. Projects with no treatments are in this file but have empty treatment fields.This file contains one record per treatment per project. A project with five treatments will have five records. All project fields are identical to the Projects API. Unique treatment fields include the following:treatment_database_id - unique value identifying the treatmenttreatment_owner - the group that created the treatment, multiple groups may have treatments on the same projectroute - the ALRS routebeg_lm - the ALRS beginning milepointend_lm - the ALRS ending milepointbeg_lm_xy - lat/long location of beginning milepointend_lm_xy - lat/long location of ending milepointtreatment_class - the parent-level category of treatmenttreatment_type - the child-level treatment type, this is the most descriptive and consistentdescription - free text description of the treatment, this may often be emptyestimated_cost - estimated treatment costprogram - the program (funds) being used to pay for the treatmentThis layer is sourced from Workflow Manager and is refreshed

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

A treatment is a specific location and scope of work or activity within a project. For example if a project was applying a microsurface to half of the project and chip seal to the other half, they would create two treatments, one identifying each activity or work type. Treatments do not correlate directly to routes but are often one-to-one. Projects with no treatments are in this file but have empty treatment fields.This file contains one record per treatment per project. A project with five treatments will have five records. All project fields are identical to the Projects API. Unique treatment fields include the following:treatment_database_id - unique value identifying the treatmenttreatment_owner - the group that created the treatment, multiple groups may have treatments on the same projectroute - the ALRS routebeg_lm - the ALRS beginning milepointend_lm - the ALRS ending milepointbeg_lm_xy - lat/long location of beginning milepointend_lm_xy - lat/long location of ending milepointtreatment_class - the parent-level category of treatmenttreatment_type - the child-level treatment type, this is the most descriptive and consistentdescription - free text description of the treatment, this may often be emptyestimated_cost - estimated treatment costprogram - the program (funds) being used to pay for the treatmentThis layer is sourced from Workflow Manager and is refreshed

Utility GIS-ADMS Integration Services Market Outlook

According to our latest research, the global Utility GIS-ADMS Integration Services market size reached USD 2.18 billion in 2024, reflecting a robust demand for integrated solutions across utility sectors. The market is expected to grow at a CAGR of 13.2% during the forecast period, reaching an estimated USD 6.01 billion by 2033. The primary growth factor driving this expansion is the increasing digital transformation initiatives adopted by utilities worldwide, aiming to modernize grid infrastructure and enhance operational efficiency through the seamless integration of Geographic Information Systems (GIS) and Advanced Distribution Management Systems (ADMS).

A significant growth driver for the Utility GIS-ADMS Integration Services market is the accelerating adoption of smart grid technologies by utility providers. As utilities strive to optimize resource allocation, reduce outages, and manage distributed energy resources, the integration of GIS and ADMS has become indispensable. GIS provides spatial intelligence, allowing utilities to map and monitor infrastructure in real-time, while ADMS offers advanced analytics and automation for distribution networks. The combination of these technologies enables utilities to make data-driven decisions, enhance outage management, and improve customer service. Moreover, regulatory pressure for grid modernization and the shift towards renewable energy sources are pushing utilities to invest in integrated digital solutions, further fueling market growth.

Another key factor propelling the Utility GIS-ADMS Integration Services market is the rising complexity of utility networks, particularly with the proliferation of distributed energy resources such as solar panels, wind turbines, and electric vehicles. Traditional grid management systems are increasingly inadequate to handle this complexity. Integration services bridge the gap by enabling seamless data exchange and workflow automation between GIS and ADMS platforms. This integration supports advanced functionalities such as predictive maintenance, real-time network visualization, and proactive outage response. The growing emphasis on grid resilience and reliability, especially in regions prone to natural disasters, is also driving utilities to adopt integrated solutions that can help mitigate risks and ensure continuous service delivery.

The surge in urbanization and the expansion of utility infrastructure in emerging economies present substantial opportunities for the Utility GIS-ADMS Integration Services market. Countries in Asia Pacific, Latin America, and parts of Africa are investing heavily in utility infrastructure to meet the demands of rapidly growing populations and industries. These regions often face challenges such as aging infrastructure, high technical losses, and limited visibility into network operations. Integration services enable utilities to overcome these challenges by providing comprehensive tools for asset management, network planning, and outage response. Additionally, government initiatives aimed at promoting smart cities and sustainable energy practices are contributing to increased adoption of GIS-ADMS integration in these markets.

From a regional perspective, North America currently leads the Utility GIS-ADMS Integration Services market, driven by early adoption of advanced utility management technologies and strong regulatory support for grid modernization. Europe follows closely, with significant investments in renewable energy integration and grid resilience initiatives. Asia Pacific is emerging as a high-growth region, propelled by large-scale utility infrastructure projects and the rapid digitization of utility operations. The Middle East & Africa and Latin America are also witnessing increased adoption, albeit at a slower pace, due to ongoing infrastructure development and government-led modernization programs. Each region presents unique growth dynamics, influenced by regulatory frameworks, technology adoption rates, and investment priorities.

Service Type Analysis

The Utility GIS-ADMS Integration Services market is segmented by service type into System Integration, Consulting, Support & Maintenance, and Others. System Integration services dominate this segment, accounting for the largest share of market revenues in 2024. The demand for system integration is fueled by the need for seamless interoperability between GIS and ADMS

Land Management Software Market size was valued at USD 1.69 Billion in 2024 and is projected to reach USD 2.62 Billion by 2031, growing at a CAGR of 5.65% from 2024 to 2031.

The growth of land management software is primarily driven by the increasing demand for efficient land use, advancements in geospatial technology, regulatory compliance, and the need for data-driven decision-making. As global populations grow and urbanization accelerates, there is a growing need for efficient land resource management. Land management software offers tools to optimize land use, enhance productivity in agriculture, forestry, and urban planning, and ensure sustainable development practices.

Advancements in geospatial technology, such as Geographic Information Systems (GIS), remote sensing, and satellite imagery, have significantly enhanced the capabilities of land management software, enabling more accurate mapping, monitoring, and analysis of land resources. Regulatory compliance and environmental concerns also drive the adoption of land management software among government agencies, landowners, and businesses.

Data-driven decision-making is another driving factor, as land management software provides powerful analytical tools for processing large volumes of spatial data, generating insights, and supporting data-driven decision-making processes. The growing awareness of climate change risks and the need for resilient land management practices drives the adoption of software solutions that enable climate-smart land management.

Precision agriculture practices are increasingly emphasized in the agricultural sector, with land management software playing a critical role in supporting these practices. The emergence of integrated land management platforms that combine GIS, asset management, and workflow automation capabilities is also driving the adoption of comprehensive software solutions.

In conclusion, the growth of land management software is driven by the need for efficient land use, advancements in technology, regulatory requirements, and the recognition of the importance of sustainable land management practices in addressing global challenges such as food security, environmental degradation, and climate change.

This dataset is created as part of an FME workflow for the City of Austin's Transportation and Planning, Parking Department. It includes polygons and buffered street segment features from the "PLANNINGCADASTRE_amanda_row_permits" dataset, which is part of the Amanda ROW permitting process that is managed by CTM and updated regularly. This dataset provides a filtered view to help visualize permitting that affected parking areas and spaces. The FME workflow updates the dataset weekly from the main CTM source to track active parking permits across the city.